GAUSS and Matlab codes for Solution of Finite-Horizon Multivariate Linear Rational Expectations Models and Sparse Linear Systems

Michael Binder () and M Hashem Pesaran ()

QM&RBC Codes from Quantitative Macroeconomics & Real Business Cycles

Abstract: Code for article published in Journal of Economic Dynamics and Control, 24 (2000), pp. 325–3. Currently, you may download four GAUSS programs and four MATLAB programs from this page. The programs CESLDU.PRG (GAUSS) and CESLDU.M (MATLAB) solve the expenditure share problem described in Section 6 of the paper using the numerical scheme of Proposition 5.1 in the paper, and are based on the canonical form (6.10) in the paper. The programs CESLDUR.PRG (GAUSS) and CESLDUR.M (MATLAB) also solve the expenditure share problem described in Section 6 of the paper using the numerical scheme of Proposition 5.1 in the paper, but are based on the canonical form (6.14) in the paper. The remaining programs illustrate how one may use the numerical schemes discussed in the paper for the solution of infinite-horizon multivariate linear rational expectations models. The programs SGLDU.PRG (GAUSS) and SGLDU.M (MATLAB) solve a simple infinite-horizon stochastic growth model using the numerical scheme of Proposition 5.1 in the paper. The programs SGBOWDEN.PRG (GAUSS) and SGBOWDEN.M (MATLAB) solve the same stochastic growth model using the numerical scheme of Proposition 5.2 in the paper. To run either one of the GAUSS programs for the stochastic growth model, you will also need to donwload the procedure MATPOW.G. The stochastic growth model solved in these programs and some further issues that arise when using the numerical schemes of this paper for the solution of infinite-horizon multivariate linear rational expectations models are discussed in a note that is also available.

Language: GAUSS; Matlab
Date: Written

Downloads: (external link)
http://dge.repec.org/codes/binder/jedc/ program code (text/plain)
none

Related works:
Working Paper: Solution of Multivariate Linear Rational Expectations Models and Large Sparse Linear Systems (1997)
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Persistent link: http://EconPapers.repec.org/RePEc:dge:qmrbcd:72

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